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How Mature Is the Field of Machine Learning?

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AI*IA 2013: Advances in Artificial Intelligence (AI*IA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8249))

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Abstract

We propose to address the question whether the fields of machine learning and pattern recognition have achieved the level of maturity in the sense suggested by Thomas Kuhn. This is inextricably tied to the notion of a paradigm, one of the cornerstones of twentieth-century philosophy of science, which however is notoriously ambiguous, and we shall argue that the answer to our inquiry does depend on the specific interpretation chosen. Here we shall focus on a “broad” interpretation of the term, which implies a profound commitment to a set of beliefs and values. Our motivating question can in fact be seen simply as an excuse to analyze the current status of the machine learning field using Kuhn’s image of scientific progress, and to discuss the philosophical underpinnings of much of contemporary machine learning research.

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References

  1. Balcan, M., Blum, A., Srebro, N.: A theory of learning with similarity functions. Machine Learning 72, 89–112 (2008)

    Article  Google Scholar 

  2. Biederman, I.: Recognition-by-components: A theory of human image understanding. Psychological Review 94, 115–147 (1987)

    Article  Google Scholar 

  3. Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  4. Bridgman, P.W.: The Logic of Modern Physics. MacMillan, New York (1927)

    Google Scholar 

  5. Bunge, M.: Technology as applied science. Technology and Culture 7, 329–347 (1966)

    Article  Google Scholar 

  6. Crick, F.: The recent excitement about neural networks. Nature 337, 129–132 (1989)

    Article  Google Scholar 

  7. Cristianini, N.: On the current paradigm in artificial intelligence. AICom (in press)

    Google Scholar 

  8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley & Sons, New York (2000)

    Google Scholar 

  9. Duin, R.: Machine learning and pattern recognition, http://www.37steps.com

  10. Duin, R., Pekalska, E.: The science of pattern recognition: Achievements and perspectives. In: Duch, W., Mandziuk, J. (eds.) Challenges for Computational Intelligence. SCI, vol. 63, pp. 221–259. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Feyerabend, P.: Against Method. New Left Books, London (1975)

    Google Scholar 

  12. Franssen, M., Lokhorst, G.J., van de Poel, I.: Philosophy of technology. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2010)

    Google Scholar 

  13. Goldstone, R.L., Son, J.Y.: Similarity. In: Holyoak, K., Morrison, R. (eds.) The Cambridge Handbook of Thinking and Reasoning, pp. 13–36. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  14. Hummel, R.A., Zucker, S.W.: On the foundations of relaxation labeling processes. IEEE Trans. Pattern Anal. Machine Intell. 5, 267–287 (1983)

    Article  MATH  Google Scholar 

  15. Jacobs, D.W., Weinshall, D., Gdalyahu, Y.: Classification with nonmetric distances: Image retrieval and class representation. IEEE Trans. Pattern Anal. Machine Intell. 22, 583–600 (2000)

    Article  Google Scholar 

  16. Jain, R.C., Binford, T.O.: Ignorance, myopia, and naiveté in computer vision systems. Computer Vision, Graphics, and Image Processing: Image Understanding 53(1), 112–117 (1991)

    MATH  Google Scholar 

  17. James, W.: The Principles of Psychology. Harvard University Press, Cambridge (1983); Originally published in 1890

    Google Scholar 

  18. Kuhn, T.S.: The Structure of Scientific Revolutions, 2nd edn. The University of Chicago Press, Chicago (1970)

    Google Scholar 

  19. Kuhn, T.S.: What are scientific revolutions? In: The Road Since Structure, pp. 13–32. The University of Chicago Press, Chicago (2000)

    Google Scholar 

  20. Lakatos, I.: The Methodology of Scientific Research Programmes. Cambridge University Press, Cambridge (1978)

    Book  MATH  Google Scholar 

  21. Lakoff, G.: Women, Fire, and Dangerous Things: What Categories Reveal about the Mind. The University of Chicago Press, Chicago (1987)

    Book  Google Scholar 

  22. Langley, P.: Machine learning as an experimental science. Mach. Learn. 3, 5–8 (1988)

    Google Scholar 

  23. Laudan, L.: Progress and Its Problems: Towards a Theory of Scientific Growth. University of California Press, Berkeley (1978)

    Google Scholar 

  24. Mayr, E.: The Growth of Biological Thought. Harvard University Press, Cambridge (1982)

    Google Scholar 

  25. Oliva, A., Torralba, A.: The role of context in object recognition. Trend Cognit. Sci. 11, 520–527 (2007)

    Article  Google Scholar 

  26. Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Machine Intell. 29(1), 167–172 (2007)

    Article  Google Scholar 

  27. Pavlidis, T.: 36 years on the pattern recognition front: Lecture given at ICPR’2000 in Barcelona, Spain on the occasion of receiving the K.S. Fu prize. Pattern Recognition Letters 24, 1–7 (2003)

    Article  Google Scholar 

  28. Pekalska, E., Duin, R.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)

    MATH  Google Scholar 

  29. Pelillo, M. (ed.): Similarity-Based Pattern Analysis and Recognition. Springer, London (in press)

    Google Scholar 

  30. Pelillo, M., Scantamburlo, T., Schiaffonati, V.: Computer science between science and technology: A red herring? In: 2nd Int. Conf. on History and Philosophy of Computing, Paris, France (2013)

    Google Scholar 

  31. Popper, K.R.: The Open Society and Its Enemies. Routledge, London (1945)

    Google Scholar 

  32. Popper, K.R.: Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge, London (1963)

    Google Scholar 

  33. Price, A.W.: Contextuality in Practical Reason, Oxford (2008)

    Google Scholar 

  34. Resnik, M.D.: Mathematics as a Science of Patterns. Clarendon Press, Oxford (1997)

    MATH  Google Scholar 

  35. Rorty, R.: Philosophy and Social Hope. Penguin Books, London (1999)

    Google Scholar 

  36. Sanborn, A.N., Griffiths, T.L., Navarro, D.J.: Rational approximations to rational models: alternative algorithms for category learning. Psychological Review 117, 1144–1167 (2010)

    Article  Google Scholar 

  37. Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29, 93–106 (2008)

    Google Scholar 

  38. Serra, J.: Is pattern recognition a physical science? In: Proc. 15th International Conference on Pattern Recognition (ICPR), Barcelona, Spain, pp. 33–40 (2000)

    Google Scholar 

  39. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  40. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  41. von Luxburg, U., Williamson, R.C., Guyon, I.: Clustering: Science or art? In: JMLR: Workshop and Conference Proceedings, vol. 27, pp. 65–79 (2012)

    Google Scholar 

  42. Watanabe, S.: Pattern Recognition: Human and Mechanical. John Wiley & Sons, New York (1985)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Università Ca’ Foscari Venezia, Italy

    Marcello Pelillo & Teresa Scantamburlo

Authors
  1. Marcello Pelillo
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  2. Teresa Scantamburlo
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Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Torino, via Pessinetto 12, 10149, Torino, Italy

    Matteo Baldoni , Cristina Baroglio , Guido Boella  & Roberto Micalizio , ,  & 

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Pelillo, M., Scantamburlo, T. (2013). How Mature Is the Field of Machine Learning?. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-03524-6_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03523-9

  • Online ISBN: 978-3-319-03524-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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